Advances in artificial intelligence (AI) and machine learning are boosting the ability of predictive analytics to boost bottom lines. Does that mean that smart machines are about to replace humans in higher-complexity jobs? No doubt, smart machines are getting smarter. But even the smartest machines lack fundamental human characteristics absolutely critical to solving certain types of problems, whether human or machine. One of these key capabilities is curiosity — but how can we replicate that?

For the answer, we need to look at neuro-dynamic programming. It’s an analytic method for learning and anticipating how current and future actions are likely to contribute to a long-term cumulative reward. This technique is related to advanced AI reinforcement learning methods, which take inspiration from behaviorist psychology to connect future reward/penalty back to earlier steps in a decision-making process. That contrasts with traditional supervised learning, which attributes reward only to the current decision.

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